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Top 5 Companion Computers for UAVs

Top 5 Companion Computers for UAVs

A look at the top 5 companion computers used by drone developers.

A companion computer is a separate onboard computer that works alongside the UAV’s primary flight controller. The companion computer acts as the brains of the robot, and processes image sensor and video data for “sight” and adds context to it - enabling perception. Drones that have a companion computer onboard can process video and image data in real time and execute autonomous navigation, path planning, and Computer Vision (CV)/AI-tasks such as obstacle avoidance and object detection. Add a radio to the mix and drones can operate and stream encoded video beyond the workshop walls. Ultimately, companion computers enable developers to run advanced AI and CV applications unique to their drone use case.

In the use case of the drone, there’s another critical sub-system in play: the flight controller. The flight controller communicates with the companion computer, adding context to data like speed, direction, and altitude, also known as PVAT (Position, Velocity, Acceleration, and Time). Traditionally, a flight controller has been a separate add-on unit, but if you can find one pre-configured with a companion computer, that can be a more efficient option.

ModalAI VOXL 2

VOXL 2 is a Blue UAS Framework autopilot that fuses a PX4 or ArduPilot flight controller and companion computer powered by Qualcomm QRB5165 into one 16g system-on-module (SOM). This credit-card-sized autopilot is built in the USA and is NDAA ‘20 Section 848 compliant. VOXL 2 features 15 TOPS of AI embedded NPU and can conduct autonomous computer vision based navigation and stream real-time h.265 video over 4G/5G, WiFi, Microhard and Doodle Labs networks. Unique to the VOXL 2 is a dedicated computer vision DSP, which offloads high-power image processing, freeing up power consumption from the GPU and CPU. VOXL 2-powered drones can conduct real-time object detection, tracking, and classification, as well as other machine learning tasks out of the box. 

VOXL 2 is used by drone OEMs in the ISR, agriculture, and defense space, researchers, and individual developers. Developers can get started quickly with VOXL 2’s open documentation and compatibility with popular developer applications such as PX4, ArduPilot, Docker, OpenCV, OpenCL and TensorFlow Lite.

VOXL 2 starts at $1,249 and is available for purchase through ModalAI’s website.

NVIDIA Jetson TX2 Developer Kit

The Jetson TX2 is a 7.5-watt supercomputer on a module (SOM) that delivers 1.33 TFLOPs, NVIDIA Pascal GPU with 256 CUDA cores and ARM Cortex-A57 CPU. The Jetson TX2 is designed for embedded systems and AI applications and works with NVIDIA’s rich set of AI tools and workflows, which enable developers to train and deploy neural networks quickly. The Jetson TX2 is supported by the NVIDIA JetPack SDK and supports popular AI frameworks like TensorFlow, PyTorch and Caffe. 

Although the Jetson TX2 boasts powerful performance, the 314g SOM alone cannot power an autonomous drone. Developers who choose to use the Jetson TX2 for autonomous drone applications need to configure the Jetson TX2 with a developer kit to access additional IO ports that will enable communication to flight hardware and a separate flight controller to activate flight. These additional boards can add significant weight to a drone’s final design and should be taken into consideration during the evaluation phase.  

The Jetson TX2 developer kit has recently been discontinued. The Jetson TX2 is $490 and is available for purchase through Arrow’s website.

NVIDIA Orin Nano Developer Kit

The Orin Nano is an entry-level companion computer that delivers up to 40 TOPS of AI performance in the smallest Jetson form factor. The Orin Nano features NVIDIA Ampere GPU with 1024 CUDA cores and 32 Tensor Cores for running complex AI models and deep learning algorithms. Similar to the Jetson TX2, the Orin Nano is supported by the NVIDIA JetPack SDK and supports TensorFlow, PyTorch and Caffe. 

Similar to the Jetson TX2, the 140g Orin Nano requires a separate flight controller to activate flight. These additional boards can add significant weight to a drone’s final design and should be taken into consideration during the evaluation phase. 

The Orin Nano Developer Kit starts at $499 and is available for purchase at Arrow’s website.

Raspberry Pi 5

Raspberry Pi is a popular intro-level companion computer. At under $100, the Raspberry Pi is a great option for hobbyists, university researchers, and developers who want to get started on their low-compute project quickly at a low cost. Raspberry Pi is powered by an ARM-based processor and primarily runs on Raspberry Pi OS. Its affordability, coupled with powerful hardware and a rich ecosystem, makes it an excellent choice for both hobbyists and professional developers in the drone industry.

Some nuances with the Raspberry Pi developers should take note of is the low power GPU and lack of neural processing unit (NPU) and on-board flight controller. Developers looking to use a Raspberry Pi for autonomous AI drone applications will need to purchase a separate flight controller in order for the drone to intelligently process its surroundings. The Raspberry Pi’s lower power GPU makes it difficult to offload intense or sophisticated programming. Popular applications like PyTorch or TensorFlow Lite may experience latency because of this. 

Raspberry Pi 5 starts at $79 and is available for purchase from many approved resellers

Raspberry Pi 4

Raspberry Pi 4 is a versatile, low-cost, credit-card-sized single-board computer that is designed to promote computer science education and provide a powerful platform for various projects and applications. The open-source Raspberry Pi platform enables developers to create custom machine learning applications tailored to specific drone missions.  

Similar to the Raspberry Pi 5, Raspberry Pi 4 does not have a neural processing unit (NPU) and on-board flight controller. Developers looking to use a Raspberry Pi for autonomous AI drone applications will need to purchase a separate flight controller in order for the drone to intelligently process its surroundings. 

Raspberry Pi 4 starts at $35 and is available for purchase from many approved resellers

At a Glance Comparison: Companion Computer Developer Kits

The following table includes stats from SOM alone and separate developer kits

ModalAI VOXL 2

NVIDIA Jetson TX2

NVIDIA Orin Nano 8GB Module

Raspberry Pi 5

Raspberry Pi 4

Companion Computer Weight

16g

314g*

140g for dev kit

59g

46g

Size

72mm x 36mm

69.6mm x 45mm

70mm x 45mm

85mm x 56mm

85mm x 56mm

Processor

QRB5165

ARM Cortex-A57 MPCore

NVIDIA Orin

Broadcom BCM2712 

Quad core 64-bit ARM-Cortex A72 running at 1.5GHz

GPU

Adreno

Pascal GPU

Ampere GPU

VideoCore VII GPU

VideoCore VI 3D GPU

NPU

Hexagon

None

None

None

None

Computer Vision DSP

Yes

None

None

None

None

Built in Flight Control

Yes

None

None

None

None

Documented Support

PX4, Ardupilot

PX4, Ardupilot

PX4, Ardupilot

PX4

PX4

ROS 2

Yes

Yes

Yes

Yes 

Yes

Video Encoding

8K30

1 x 4K60

1080p30

4k60

4k60

MIPI Image Sensor Inputs

7 (onboard)

3 (via dev kit)

2 MIPI CSI-2 (via dev kit)

2 x 4 lane MIPI  (onboard)

2 x 2 lane (onboard)

Built in Sensors (IMU and Barometer)

Yes

No

No

No

No

TOPS

15

1.33 

40

None

None

AI Framework Support

TensorFlow Lite

CUDA

CUDA

TensorFlow Lite

TensorFlow Lite

Connectivity

4G, 5G, WiFi, Microhard, Doodle Labs

4G, 5G, WiFi, Microhard, Doodle Labs 

4G, 5G, WiFi, Microhard, Doodle Labs

WiFi

WiFi

Integrated Daughter Board/Hats

Yes

None

None

Yes

Yes


*Source: https://forums.developer.nvidia.com/t/weight-of-boards/175292/3 






 

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Now all VOXL products have SDK 1.1.2 parity.

Now all VOXL products have SDK 1.1.2 parity.

SDK 1.1.2 is now available on all VOXL products.

Happy New Year! We’ve been hard at work making big changes under the hood. Our latest VOXL SDK 1.1.2 is now available on all VOXL products - including our original Blue UAS Framework 1.0 VOXL line. Experience streamlined configuration and faster video processing like never before.

SDK 1.1.2 feature highlights:

APQ8096 and Qualcomm Flight RB5 5G Platform support

Now all VOXL and RB5 products run the same SDK across the board

v1.14 on Flight Core v1 and v2
Our Flight Core V1 and V2 images are now built from the same codebase as the PX4 version running on VOXL 2 SDSP ensuring predictable PX4 behavior across the product line.

    Hardware-accelerated video compression in voxl-camera-server

    For hires color cameras, voxl-camera-server can now publish either H265 or H264 compressed video over libmodal-pipe. Furthermore, it can compress two video streams at different resolutions simultaneously. This enables, for example, one SD video stream for RTSP alongside one HD video stream for recording to disk with the new voxl-record-video tool. Both at 30hz, accelerated in hardware! This enabled the next feature:

      Always-on RTSP video and MAVLink camera protocol support

      Out of the box, in hardware configurations with a color hires camera, voxl-streamer and voxl-mavcam-manager are enabled by default so that as soon as you connect to QGroundControl you get a live video feed with no additional configuration steps. Furthermore, the video snapshot and video record functions in QGroundControl will automatically appear and allow you to take full resolution JPEG snapshots and HD video recordings that get saved to disk onboard VOXL in flight.

        Streamlined configuration

        A primary focus has been to streamline MPA configuration process by including it in the SDK install script. A new package, voxl-configurator consolidates tools for managing the SKUcalibration files, and configuring MPA services. All of these tools are called during the SDK flashing script

          Other

          • Improved thresholding in voxl-calibrate-camera that’s less sensitive to lighting conditions
          • 16-bit raw lepton thermal camera data pipe
          • Send VIO packets with quality=-1 to PX4 on VIO failure to utilize PX4’s new quality metric functionality and make for more graceful fallback to altitude flight mode.
          • Robustness improvements to libmodal_pipe